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Article

Characterization of Microstructures in Lacustrine Organic-Rich Shale Using Micro-CT Images: Qingshankou Formation in Songliao Basin

by
Yan Cao
1,2,
Qi Wu
3,
Zhijun Jin
1,2,* and
Rukai Zhu
4
1
Institute of Energy, Peking University, Beijing 100871, China
2
School of Earth and Space Sciences, Peking University, Beijing 100871, China
3
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China
4
Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Energies 2022, 15(18), 6712; https://doi.org/10.3390/en15186712
Submission received: 23 July 2022 / Revised: 6 September 2022 / Accepted: 7 September 2022 / Published: 14 September 2022
(This article belongs to the Special Issue Petroleum Engineering in Oil and Gas Production: Advances in Theory and Operation)
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Abstract

:
In order to explore the development characteristics and influencing factors of microscale pores in lacustrine organic-rich muddy shale, this study selected five shale samples with different mineral compositions from the Qingshankou Formation in the Songliao Basin. The oil content and mineralogy of the shale samples were obtained by pyrolysis and X-ray diffraction analysis, respectively, while the porosity of the samples was computed by micro-CT imaging. Next, based on the CT images, the permeability of each sample was calculated by the Avizo software. Results showed that the continuous porosity of Qingshankou shale in the Songliao Basin was found between 0.84 and 7.79% (average 4.76%), the total porosity between 1.87 and 12.03% (average 8.28%), and the absolute permeability was calculated between 0.061 and 2.284 × 10−3 μm2. The total porosity of the samples has a good positive correlation with the continuous porosity and permeability. This means higher values of total porosity suggested better continuous porosity and permeability. Both total porosity and continuous porosity are positively correlated with the content of clay minerals. Moreover, the oil content of the samples (the S1 peak from programmed pyrolysis) exhibits a good positive correlation with the total porosity, continuous porosity, permeability, and clay mineral content. Therefore, pores that are developed by clay minerals are the main storage space for oil and flow conduits as well. Clay minerals were found to be the main controlling factor in the porosity, permeability, and the amount of oil content in the pores in the study area.

1. Introduction

Shale oil and gas resources are abundant around the globe, and drilling horizontally and hydraulic fracturing have been applied successfully in North America, enabling large-scale commercial development of shale oil and gas [1,2], and accounting for a significant growth in hydrocarbon production. In 2018, the global crude oil production was 44.5 × 108 t, of which 14% was from unconventional shale plays. Additionally, natural gas production was 3.97 × 1012 m3, with 25% from unconventional plays [3]. In addition to the US, China is also rich in plays with a huge exploration potential, heading towards large-scale production [1,2].
The shale oil–producing layers in North America are mainly distributed in marine or foreland basins, in a large depositional area, with good continuity, mostly over pressured and high thermal maturity. Conversely, in China, such plays are mostly distributed in depressions and rift basins of continental deposition, which are characterized by strong heterogeneity, low overall pore pressure, and lower thermal maturity [4]. Generally speaking, regardless of the basin in China, shale reservoirs have ultra-low permeability (1 × 10−9~1 × 10−4 μm2), nanoscale pore diameter (1~200 nm), and complex pore structures [5]. The pore structure and permeability in shale are often considered key indicators of the storage space and flow capacity of shale oil and gas [1,2,5]. Therefore, the pore structure and permeability of Chinese terrestrial shales have been a hot topic of research [1,2,5].
A large number of research tools have been used to reveal the pores of shale, including scanning electric microscopes (SEM) [5], atomic force microscopes (AFM) [6], mercury intrusion porosimetry (MIP) [7], low-pressure gas adsorption [8], micro-CT and nano-CT [9,10], small-angle X-ray or neutron scattering [11], and so on. Loucks et al. (2009) [5] reported that the pores of Mississippian Barnett shale are mainly nanoscale (nanopores), and the pore size can be as low as 5 nm. Organic matter pore space is the dominant pore type in the shale and is strongly controlled by maturity, and pyrite and shale matrix can also provide some pore space for shale [5]. There are several factors that control the enrichment of gas in the marine shales, including well-developed micropores, a large surface area, and a high gas adsorption capacity [8]. Inter-particle pores between organic matter and clay minerals may be responsible for those three controls [8]. Sun et al. (2018) [11] reported that the storage and flow mechanisms of gas in shale reservoirs can be greatly affected by pore structure. There were rarely closed pores in illite but mainly in organic matter by means of small-angle neutron scattering (SANS) [11]. Moreover, geometrical tortuosity and matrix permeability are negatively correlated with the fraction of closed pores [11]. However, most of the current methods only reveal shale pore space but ignore the pore connectivity of shale; furthermore, shale oil and gas development depends heavily on pore connectivity [12]. Among those means, micro-CT and 3D reconstruction of pore spaces via digital rock physics (DRP) methods can effectively distinguish various type of pores, including the continuous pores and isolated ones [13].
In confined nanopores of shale, the solid-liquid intermolecular forces result in complex fluid properties which impact fluid flow. This means the traditional macroscopic Darcy flow equation is no longer applicable for accurately characterizing the fluid flow in such confined very fine spaces [13,14], which has encouraged a large amount of research [15,16,17,18,19]. Currently, the permeability of shales can be determined in the laboratory using three methods, including (i) gas measurements of plunger cores, (ii) gas analysis of particle samples, and (iii) the use of mercury (Hg) intrusion curves [20]. However, it is not feasible to measure shale permeability using steady-flow methods because of measurement of extremely small pressure drops or flow rates requires highly complex instrumentation. The development of pulse decay techniques followed, which could measure pressure decay on an upstream end of a confined core as well as pressure increase on the downstream end. As little as 10−9 millidarcies (1 × 10−15 mD ≈ 1 m2) of permeability can be measured with pulse decay techniques in minutes to hours or even days, depending on the application [21]. Helium (He) is utilized for permeability measurements of granular samples of shale, and pressure decay is measured and quantified as permeability [22]. The particle density or skeletal density of the rock is obtained by He porosimetry using crushed samples, and the porosity of shale can also be calculated by combining the capacity of the impregnated mercury [23]. Numerous studies have examined the relationship between permeability and Hg injection curves [24,25]. Permeability is calculated based on Hg saturation and capillary pressure at the apex of the hyperbolic log-log Hg injection plot [26]. Although the above experimental methods can characterize the permeability of shale to some extent, there are still problems such as large errors and low reproducibility. Considering low saturation and mobility of oil in shale, it would be difficult to intuitively capture oil flow in shale samples in experimental studies. Therefore, theoretical methods via simulation of the flow process [19] were used to investigate permeability in our selected shales.
The shale of the Qingshankou Formation in the Gulong Sag is a key target for shale oil development in China. The relationship between total porosity, continuous porosity, and permeability in studied shale has not been previously revealed by the combination of micro-CT experiments and simulations. Therefore, it is necessary to reveal the characteristics of porosity and permeability of shale in the region and its influence factors through a combination of experiments and simulations.
In this study, five shale samples from the Well Songyeyou 1HF, which was drilled through the Qing 1–3 Member in the Gulong Sag of the Songliao Basin, were selected for analysis and testing. Through micro-CT imaging combined with modeling, the difference in porosity, permeability, and oil content of the shale samples with different mineral assemblages is recognized first, then flow behavior in each is studied by simulation method to enable us to judge the possibility of commercial development of shale layers in the block.

2. Geological Setting and Sampling

The Songliao Basin is a large-scale lacustrine facies basin located in northeastern China (Figure 1A). There were three major tectonic stages in the formation of the basin: fault subsidence, thermal subsidence, and inversion [27]. The basin can be divided into 6 tectonic units: the western slope, northern steep slope, central depression, northeast uplift belt, southeast uplift belt, and southwest uplift belt (Figure 1B). The Gulong Sag is located in the western side of the Central Sag, which was formed during the depositional period of the Qingshankou Formation, the main source rock in the basin (Figure 1C). The point B’ (i.e., red star), the well location of the studied samples, is located at the junction between profile AA’ and BB’ (Figure 1C).
The Qingshankou Formation (K2qn), a moderately deep lacustrine environment, was influenced by periodic marine intrusions. The lithology divides the Qingshankou Formation (K2qn) into three subsections (K2qn1, K2qn2, and K2qn3) based on lithology (Figure 2). The first member of the Qingshankou Formation (section K2qn1) is widely distributed throughout the basin and is one of the most favorable hydrocarbon source rocks in the Songliao Basin [27]. The first member of the Qingshankou Formation (section K2qn1) is up to 500 m thick, and formed during rapid, large-scale lake transgression. Lake levels rose several times due to stepwise subsidence of the basement during the emplacement of K2qn1, leading to the interbedded accumulation of dark shale and siltstone [27]. The first member of the Qingshankou Formation (section K2qn1) was selected as the target layer for this study, and five typical dark shale samples in this member were selected for our study.

3. Experiments

3.1. Analysis of Oil Content and Mineral Content

Oiliness analysis is performed following the ASTM standards [28]. Next, samples were powdered to the mesh size of 100 and were analyzed for thermal maturity with programmed pyrolysis. This procedure provided us the S1 peak (mg HC/g rock), which is the quantity of free hydrocarbons volatized at 300 °C. This peak can represent the oiliness of the sample. Furthermore, powder finer than 200 mesh (i.e., <0.075 mm) was analyzed by quantitative X-ray diffraction (XRD) to determine the mineral content of the studied samples. The D/max-2500 diffractometer was used for the measurements, following two separate CPSC procedures [29].

3.2. Micro CT Experiment

First, a cylindrical core plug with a diameter of 1 mm and a length of 1 mm parallel to the bedding from the main core was retrieved. The micro-CT imaging was completed in the China Petroleum Exploration and Development Research Institute using the Nano-CTX Radia scanning equipment (Model Ultra XRM-L200) from ZEISS, with a maximum resolution of 1μm. During the scanning process, the sample is rotated from −90 to 90° and the X-ray information is continuously acquired [30].
In the micro-CT experiment, the scanning voltage was set to 8 kV, at 20 °C, and the exposure time was 90 s. A total of 901 two-dimensional plane images along the Z-axis were obtained, which can be stacked to form a three-dimensional data volume with a diameter of 65 μm and a height of 60 μm [30]. Using the Avizo software, the 3D model of the sample can be reconstructed which is shown in Figure 3 for the S41 sample. In this study, we analyzed five shale samples from the Qingshankou Formation in the Gulong Sag labeled as: S41, S189, S201, S317, and S353.

3.3. Avizo Simulation Computing

Avizo software was used to reconstruct the 3D shale models from cross-sectional images from the micro-CT data. To separate pores from the matrix, the threshold segmentation method based on the gray scale was used to select using the Avizo software. To be more specific, the Gaussian deconvolution threshold segmentation method for identifying different phases in the CT images was employed which converts each image into binary mode and further will be used to reconstruct the pore structure network. Figure 4 represents the process that was performed for the S41 sample as an example, where the blue part is the pore distribution extracted from the area with higher values on the gray scale.
Th permeability of the reservoir refers to the property of the rock that allows fluid to pass through its continuous pores under a certain pressure difference. In other words, permeability refers to the conductivity of the rock to fluids. The permeability of the reservoir determines the ease of hydrocarbon penetration, which is one of the main parameters for evaluating reservoir quality. In the petroleum industry, absolute permeability is a common parameter that has been used frequently as a measure of the reservoir productivity. This parameter can be calculated from the CT-based 3D models which can be verified with experimental analysis as well. Flow experiments on core samples were conducted under steady state, and the following equation was used to calculate permeability using Darcy’s law:
k g = 2 p a μ q g L P 1 2 − P 2 2 A
v g = q g A = P 1 2 − P 2 2 L × k g 2 p a μ
In order to make sure that the flow conditions satisfy Darcy’s Law, tests were carried out under different flow rates. In practice, permeability is calculated from the slope of the curve of flow velocity,   v g vs ( P 1 2 − P 2 2 ) / L . A similar approach can be followed in numerical simulation as well. In the simulation models, air density in the flow process was ignored and the flow was considered incompressible viscous, which satisfies the three laws of mass, momentum, and energy conservation. Therefore, the flow can be described by the Navier–Stokes equation, which is defined by the following formula:
ρ ∂ v ∂ t + v · ∇ v = ρ f − ∇ p + μ ∇ 2 v
Using Equation (2), if q g is known, permeability k g can be computed. Hence, porous media parameters such as the pore structure and permeability can be easily estimated from the digital shale model.

3.4. The Porosity and Permeability of the Sample by Conventional Method

Based on a rock sample’s bulk volume, grain volume, and pore volume, the porosity is calculated. Porosity studies were per-formed using typical equipment and conventional methods (i.e., GRI [31,32]).
Using virtually the same method as GRI, samples were analyzed in this laboratory [33]. Samples were weighed to a precision of 0.001 g and their bulk volumes measured to a precision of 0.001 cm3. In the next step, a core plug was drilled perpendicular to the lamination. After crushing the remaining sample with a mechanical rock crusher, the 20/35 US mesh fraction was sieved. In order to limit the evaporation of fluids from the sample, these steps were performed as quickly as possible. The 20/35 fraction was then divided into two subsamples and sealed in airtight vials. Using the GRI method, one subsample was measured for porosity and permeability. Afterwards, a second subsample was refluxed for 7 days in toluene in a Dean Stark apparatus. Water extraction was verified twice a day by checking fluid volumes. After being dried for 2 weeks at 110 °C, the samples were weighed until weight stabilization (0.001 g) was achieved. After that, the samples were kept in a desiccator. Helium gas at approximately 200 psig was used for measuring permeability. We measured pressure at 0.25 s intervals for a maximum of 2000 s. In the end, we measured the permeability of core plugs with the PDP technique described by Jones [34] at a helium pressure of 1000 psi and confining pressure of 5000 psi [33].

3.5. Experimental Procedure for Studying Samples

Combining above mentioned experiments, we summarize the schematic diagram of the experimental procedure of the studied sample in Figure 5. The porosity and permeability of the studied samples were obtained by means of micro-CT and simulation calculations, respectively. The reliability of micro-CT and simulation is verified by comparing the reservoir characteristics obtained above with the porosity and permeability measured by typical experimental equipment. Correlation of reservoir characteristics with XRD results and free hydrocarbon S1 to obtain control factors of shale reservoir.

4. Results

4.1. Mineral Composition and Oil Content

XRD results confirm all five samples from the Qingshankou Formation in the Gulong Sag consisted of large amounts of clay, varying between 32.6 wt. and 42.3 wt.%, with an average value of 36.46 wt.%. In addition, quartz is also abundant in the samples ranging between 29.9 and 34 wt.%, with an average of 32.4 wt.%, and plagioclase (18.1–23.4 wt.%, with and average of 20.16 wt.%) was also detected in the samples. Other minerals, including calcite, siderite, and pyrite, were detected with different amounts in the samples. Movable hydrocarbon (S1) content was found relatively high, mainly at 1.36–8.54 mg/g, with an average of 5.67 mg/g. The detailed mineralogy and oil-bearing characteristics of all samples are summarized in Table 1.

4.2. Porosity and Permeability

Using DRP methods and the 3D model that was obtained by Avizo software, the total and effective porosity of five samples is calculated and reported in Table 2. Furthermore, the flow simulation module provided by the Avizo software can compute the permeability of the samples which is also listed in Table 2. Results showed that these values vary notably among the samples. The continuous porosity of these five samples was found between 0.84 and 7.79% (average 4.76%), the total porosity between 1.87 and 12.03% (average 8.28%), and the absolute permeability was calculated between 0.061 and 2.284 × 10−3 μm2. Moreover, average open pores account for 55.79% of the total pores (Table 2), which means the average porosity in this area is relatively low, while the proportion of continuous pores is high, and the permeability is low. Based on the results we can categorize the shale samples in the study area as tight reservoir with low porosity and permeability.
The measured results of porosity and permeability of the samples using the typical equipment are shown in Table 3, and the above test results are similar to those obtained by micro-CT and simulation; the error produced by comparing the results of the two methods is acceptable (Table 2 and Table 3). Therefore, the combination of micro-CT and simulation is a reliable method to reveal shale porosity and permeability.
Based on the above summary, the low saturation and mobility of oil and gas in shale makes it difficult to obtain plausible permeability in shale samples in conventional experimental studies (i.e., gas measurements of plunger core, gas analysis of particle sample, and the use of mercury (Hg) intrusion curves). However, the combination of micro-CT and simulation calculation could obtain credible total porosity, continuous porosity, and permeability of shale samples, while avoiding the consumption caused by permeability experiments.

5. Discussions

5.1. Relationship between Porosity and Permeability

Previous studies have shown that porosity characterizes the storage capacity of the reservoir rock [35]. However, if the porosity of the tight reservoirs is too large, the pore-throat ratio increases, which makes it difficult to establish an effective driving pressure difference during production. Therefore, it is necessary to comprehensively evaluate the porosity of the rock samples from the perspective of reservoir property and flow behavior during reservoir quality evaluation [35]. In this study, we found that the total porosity and the continuous porosity have a linear relationship (Figure 6a), which means higher total porosity leads to more developed continuous pores. Total porosity and continuous porosity were both significantly positively correlated with permeability (Figure 6b,c), which indicates higher values of total porosity and continuous porosity suggested better permeability. Findings from this study are also consistent with the results from Burnham (2017) [36], which studies shale samples from the Green River Formation. In addition, our study found no positive correlation between the total porosity of the studies samples and the burial depth (Figure 6d), which indicates the effect of formation compaction on the porosity of shale in the study area is not obvious. The possible reason is that the hydrocarbon generation of organic matter in the studied strata generates strong pressure, which could counteract the compaction of the overlying strata [37].

5.2. Relationship between Porosity and Mineral Content

Previous studies have explained that clay minerals are the main constituent component of shale and closely control the occurrence and enrichment of shale plays [38,39]. Considering clay minerals, their special crystal structure causes different types of pores to form between their crystal layers, also creating inter and intraparticle pore spaces. Furthermore, size, morphology and specific surface area of these pores determine the shale oil storage capacity. Previously it was documented that clay minerals are mostly composed of different types of porous structures, i.e., montmorillonite mostly develops in a circular and slit-like meso/micropores and has the largest total specific surface area. As the burial temperature increases, it transforms into mixed layers of other clay minerals, specifically illite, and the number of pores in the corresponding clay minerals gradually decreases [39]. Kaolinite is dominated by medium and large primary pores of 20–100 nm in size, which can be altered into illite in an alkaline environment [39]. Mesopores and macropores are mostly developed in illite and kaolinite. Additionally, it was found that the content of clay minerals in the same TOC range has a positive correlation with the pore volume and pore specific surface area of the organic-rich shale [40], and the pores between clay minerals can be filled with organic matter that migrates over a short distance. Therefore, pores of organo–clay mineral nanocomponents and organo–clay mineral complexes [41,42] can be considered as the main contributors to shale pore structure. Therefore, in general, pores of clay minerals are in general very well developed [43,44], which is the main controlling factor for the development of various types of pores in shale. In addition, previous studies found that illite and kaolinite are the main clay types in the shales of the Qingshankou Formation of the Gulong Sag [45]. Comparing our results with previous studies confirm that the total and continuous porosity of the samples in this study have a linear relationship with the content of clay minerals (Figure 7a,b), which means clay minerals are providing adequate pore space to the for the fluid to flow as well as storing the generated hydrocarbons. Our findings echo, to some extent, those of previous studies that suggested the variation of shale permeability depends on the nature of the clay mineral surface [46], the presence of a large number of pore structures in typical clay minerals (i.e., illite and chlorite) in shale has a positive effect on permeability [47]. Notably, our porosity values are lower than the typical porosity range of shale in the depth, according to porosity data compilation in Kim et al. [48], which could be attributed to a sequential microquartz cementation process and the quartz cement preferentially blocked the small mudstone pores during diagenesis [49].

5.3. Relationship between Porosity and Oil-Bearing S1

High-yield shale oil formations are often accompanied by a variety of minerals such as kaolinite and Fe2O3 that can promote hydrocarbon generation and transformation [50]. Natural attapulgite, kaolinite, clinoptilolite, and other minerals can catalyze the in situ upgrading of oil shale. Moreover, clay minerals are naturally micro-mesoporous materials with good thermochemical stability and are widely used as catalyst carriers and adsorbents [51,52], which not only improves the hydrocarbon production from shale oil but also reduces the activation energy [53,54,55,56], promoting the thermal maturity of oil shale kerogen and increasing the oil generation from the source rock. In this regard, simulation studies argue that montmorillonite has a catalytic effect on hydrocarbon generation from the organic matter [57], while the catalytic ability of illites is relatively small. These clay-based catalysts have good thermal stability and a simple development process and show significant potential in improving oil shale conversion efficiency and hydrocarbon yield [58] which makes us to conclude that the value of S1 with the amount of clay minerals in the samples should be closely related.
Shale oil mainly accumulates in matrix pores, and there is a positive correlation between pore development and the S1 peak [40,43]. Clay minerals have been shown to have a high adsorption capacity for shale oil [59]. In this regard, hydrocarbons show different adsorption properties in the pore structure of clay minerals where the adsorption capacity of montmorillonite for hydrocarbons is stronger than that of illite and kaolinite. In addition to the pore size of the adsorbent, the structure and morphology of hydrocarbon molecules also strongly affects the adsorption behavior [56]. However, clay mineral content and pore volume are negatively correlated, according to some studies, which indicates developing large pores or enriching movable oil in clay is not possible [60].
Here, we found that the oil content of the samples (S1) has a good linear relationship with the total and continuous porosity as well as the permeability and the clay content of the samples (Figure 8). Therefore, we can conclude the pores that are developed by clay minerals are the main storage space for oil in these samples.

6. Conclusions

In order to obtain the pore and permeability characteristics of the shales in the Qingshankou Formation and their relationship with oil-bearing, micro-CT and simulation calculations, instead of traditional experiments, were performed on five typical samples. Based on the results the following conclusions can be made:
(1)
The permeability obtained using micro-CT and simulation approximate those obtained with typical equipment. The combination of micro-CT and simulation in this study is reliable for revealing shale reservoir characteristics.
(2)
The continuous porosity of Qingshankou shale in the Songliao Basin was found between 0.84 and 7.79% (average 4.76%), the total porosity between 1.87 and 12.03% (average 8.28%), and the absolute permeability was calculated between 0.061 and 2.284 × 10−3 μm2. The total porosity of the Qingshankou shale in the Songliao Basin has a good positive correlation with the continuous porosity and permeability. Higher the total porosity in the study area denotes better development of continuous pores, thus higher permeability.
(3)
Both total and continuous porosity of the samples are positively correlated with the content of clay minerals. The pores developed by clay minerals are the main space for oil storage in these shale samples, and the amount of clay minerals would be the main controlling parameter that impacts samples porosity, permeability, and oil content in the study area.

Author Contributions

Conceptualization, Y.C.; Data curation, Y.C. and R.Z.; Formal analysis, Y.C.; Funding acquisition, Z.J.; Investigation, Z.J. and R.Z.; Methodology, Q.W. and R.Z.; Resources, Z.J. and R.Z.; Software, Q.W.; Supervision, Z.J.; Writing—original draft, Y.C.; Writing—review & editing, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was jointly funded by the National Science Foundation of China (42090020/42090025) and the 2022 American Association of Petroleum Geologists Foundation Grants-in-Aid Program (Grants-in-Aid General Fund Grant).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cao, Y.; Han, H.; Liu, H.-W.; Jia, J.-C.; Zhang, W.; Liu, P.-W.; Ding, Z.-G.; Chen, S.-J.; Lu, J.-G.; Gao, Y. Influence of solvents on pore structure and methane adsorption capacity of lacustrine shales: An example from a Chang 7 shale sample in the Ordos Basin, China. J. Pet. Sci. Eng. 2019, 178, 419–428. [Google Scholar] [CrossRef]
  2. Loucks, R.G.; Reed, R.M.; Ruppel, S.C.; Hammes, U. Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG Bull. 2012, 96, 1071–1098. [Google Scholar] [CrossRef]
  3. Zou, C.N.; Pan, S.Q.; Jing, Z.H.; Gao, J.L.; Yang, Z.; Wu, S.T.; Zhao, Q. Shale oil and gas revolution and its impact (in Chinese with English abstract). Acta Petrol. Sin. 2020, 41, 1–12. [Google Scholar]
  4. Jin, X.; Li, G.X.; Meng, S.W.; Wang, X.Q.; Liu, C.; Tao, J.P.; Liu, H. Microscale comprehensive evaluation of continental shale oil recoverability (in Chinese with English abstract). Pet. Explor. Dev. 2021, 48, 256–268. [Google Scholar] [CrossRef]
  5. Loucks, R.G.; Reed, R.M.; Ruppel, S.C.; Jarvie, D.M. Morphology, Genesis, and Distribution of Nanometer-Scale Pores in Siliceous Mudstones of the Mississippian Barnett Shale. J. Sediment. Res. 2009, 79, 848–861. [Google Scholar] [CrossRef]
  6. Li, C.X.; Ostadhassan, M.; Guo, S.; Gentzis, T.; Kong, L.Y. Application of PeakForce tapping mode of atomic force microscope to characterize nanomechanical properties of organic matter of the Bakken Shale. Fuel 2018, 233, 894–910. [Google Scholar] [CrossRef]
  7. Clarkson, C.R.; Solano, N.; Bustin, R.M.; Bustin, A.M.M.; Chalmers, G.R.L.; He, L.; Melnichenko, Y.B.; Radliński, A.P.; Blach, T.P. Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mer-cury intrusion. Fuel 2013, 103, 606–616. [Google Scholar] [CrossRef]
  8. Han, H.; Zhong, N.N.; Ma, Y.; Huang, C.X.; Wang, Q.; Chen, S.J.; Lu, J.G. Gas storage and controlling factors in an over-mature marine shale: A case study of the Lower Cambrian Lujiaping shale in the Dabashan arc-like thrust–fold belt, southwestern China. J. Nat. Gas Sci. Eng. 2016, 33, 839–853. [Google Scholar] [CrossRef]
  9. Sisk, C.; Diaz, E.; Walls, J.; Grader, A.; Suhrer, M. 3D Visualization and Classification of Pore Structure and Pore Filling in Gas Shales, SPE-paper 134582. In Proceedings of the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September 2010. [Google Scholar]
  10. Walls, J.D.; Diaz, E.; Cavanaugh, T. Shale reservoir properties from digital rock physics, SPE-Paper 152752. In Proceedings of the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 20–22 March 2012. [Google Scholar]
  11. Sun, M.; Yu, B.; Hu, Q.; Yang, R.; Zhang, Y.; Li, B.; Melnichenko, Y.B.; Cheng, G. Pore structure characterization of organ-ic-rich Niutitang shale from China: Small angle neutron scattering (SANS) study. Int. J. Coal Geol. 2018, 186, 115–125. [Google Scholar] [CrossRef]
  12. Wang, Y.; Pu, J.; Wang, L.; Wang, J.; Jiang, Z.; Song, Y.-F.; Wang, C.-C.; Wang, Y.F.; Jin, C. Characterization of typical 3D pore networks of Jiulaodong formation shale using nano-transmission X-ray microscopy. Fuel 2016, 170, 84–91. [Google Scholar] [CrossRef]
  13. Wu, K.L.; Chen, Z.X.; Li, J.; Li, X.F.; Xu, J.Z.; Dong, X.H. Wettability effect on nanoconfined water flow. Proc. Natl. Acad. Sci. USA 2017, 114, 3358–3363. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Wang, H.; Su, Y.L.; Wang, W.D.; Sheng, G.L.; Li, H.; Zafar, A. Enhanced water flow and apparent viscosity model consid-ering wettability and shape effects. Fuel 2019, 253, 1351–1360. [Google Scholar] [CrossRef]
  15. Wang, H.; Su, Y.L.; Zhao, Z.F.; Wang, W.D.; Sheng, G.L.; Zhan, S.Y. Apparent permeability model for shale oil transport through elliptic nanopores considering wall-oil interaction. J. Pet. Sci. Eng. 2019, 176, 1041–1052. [Google Scholar] [CrossRef]
  16. Wang, H.; Su, Y.L.; Wang, W.D.; Li, L.; Sheng, G.L.; Zhan, S.Y. Relative permeability model of oil-water flow in nanoporous media considering multi-mechanisms. J. Pet. Sci. Eng. 2019, 183, 106361. [Google Scholar] [CrossRef]
  17. Zhang, T.; Li, X.F.; Yin, Y.; He, M.X.; Liu, Q.; Huang, L.; Shi, J.T. The transport behaviors of oil in nanopores and nanoporous media of shale. Fuel 2019, 242, 305–315. [Google Scholar] [CrossRef]
  18. Cui, J.F.; Wu, K.L. Equivalent permeability of shale rocks: Simple and accurate empirical coupling of organic and inorganic matter. Chem. Eng. Sci. 2020, 216, 115491. [Google Scholar] [CrossRef]
  19. Javadpour, F.; Singh, H.; Rabbani, A.; Babaei, M.; Enayati, S. Gas Flow Models of Shale: A Review. Energy Fuels 2021, 35, 2999–3010. [Google Scholar] [CrossRef]
  20. Cui, X.; Bustin, A.M.M.; Bustin, R.M. Measurements of gas permeability and diffusivity of tight reservoir rocks: Different ap-proaches and their applications. Geofluids 2009, 9, 208–223. [Google Scholar] [CrossRef]
  21. Brace, W.F.; Walsh, J.B.; Frangos, W.T. Permeability of granite under high pressure. J. Geophys. Res. 1968, 73, 2225–2236. [Google Scholar] [CrossRef]
  22. Egermann, P.; Lenormand, R.; Longeron, D.; Zarcone, C. A fast and direct method of permeability measurements on drill cuttings. Soc. Petrol. Eng. Reserv. Eval. Eng. 2005, 4, 269–275. [Google Scholar] [CrossRef]
  23. American Petroleum Institute (API). Recommended Practices for Core Analysis—Recommended Practice 40, 2nd ed.; API: Dallas, TX, USA, 1998. [Google Scholar]
  24. Kamath, J. Evaluation of Accuracy of Estimating Air Permeability From Mercury- Injection Data. SPE Form. Eval. 1992, 7, 304–310. [Google Scholar] [CrossRef]
  25. Carles, P.; Egermann, P.; Lenormand, R.; Lombard, J.M. Low permeability measurements using steady-state and transient methods, Paper SCA2007-07. In Proceedings of the 2007 International Symposium of the Society of Core Analysis, Calgary, AB, Canada, 10–12 September 2007. [Google Scholar]
  26. Swanson, B.F. A Simple Correlation between Permeabilities and Mercury Capillary Pressures. J. Pet. Technol. 1981, 33, 2498–2504. [Google Scholar] [CrossRef]
  27. Liu, B.; Wang, H.L.; Fu, X.F.; Bai, Y.F.; Bai, L.H.; Jia, M.C.; He, B. Lithofacies and depositional setting of a highly prospective lacustrine shale oil succession from the Upper Cretaceous Qingshankou Formation in the Gulong sag, northern Songliao Basin, northeast China. AAPG Bull. 2019, 103, 405–432. [Google Scholar] [CrossRef]
  28. ASTM. Annual Book of ASTM Standards, 2010—Gaseous Fuel, Coal and Coke, 5.06; ASTM International: West Conshohocken, PA, USA, 2010. [Google Scholar]
  29. Guo, H.J.; Jia, W.L.; Peng, P.A.; Lei, Y.H.; Luo, X.R.; Cheng, M.; Wang, X.Z.; Zhang, L.X.; Jiang, C.F. The composition and its impact on the methane sorption of lacustrine shales from the Upper Triassic Yanchang Formation, Ordos Basin, China. Mar. Pet. Geol. 2014, 57, 509–520. [Google Scholar] [CrossRef]
  30. Jiang, C.B.; Niu, B.W.; Yin, G.Z.; Zhang, D.M.; Yu, T.; Wang, P. CT-based 3D reconstruction of the geometry and propagation of hydraulic fracturing in shale. J. Pet. Sci. Eng. 2019, 179, 899–911. [Google Scholar] [CrossRef]
  31. Sun, J.M.; Dong, X.; Wang, J.J.; Schmitt, D.R.; Xu, C.L.; Mohammed, T.; Chen, D.W. Measurement of total porosity for gas shales by gas injection porosimetry (GIP) method. Fuel 2016, 186, 694–707. [Google Scholar] [CrossRef]
  32. Zhou, S.; Dong, D.; Zhang, J.; Zou, C.; Tian, C.; Rui, Y.; Liu, D.X.; Jiao, P.F. Optimization of key parameters for po-rosity measurement of shale gas reservoirs. Nat. Gas Ind. B 2021, 8, 455–463. [Google Scholar] [CrossRef]
  33. Fisher, Q.; Lorinczi, P.; Grattoni, C.; Rybalcenko, K.; Crook, A.J.; Allshorn, S.; Burns, A.D.; Shafagh, I. Laboratory charac-ter-ization of the porosity and permeability of gas shales using the crushed shale method: Insights from experiments and numerical modelling. Mar. Petrol. Geol. 2017, 86, 95–110. [Google Scholar] [CrossRef]
  34. Jones, S.C. A Technique for Faster Pulse-Decay Permeability Measurements in Tight Rocks. SPE Form. Eval. 1997, 12, 19–25. [Google Scholar] [CrossRef]
  35. Pang, Y.; Soliman, M.Y.; Deng, H.; Emadi, H.C. Analysis of Effective Porosity and Effective Permeability in Shale-Gas Res-ervoirs With Consideration of Gas Adsorption and Stress Effects. SPE J. 2017, 22, 1739–1759. [Google Scholar] [CrossRef]
  36. Burnham, A.K. Porosity and permeability of Green River oil shale and their changes during retorting. Fuel 2017, 203, 208–213. [Google Scholar] [CrossRef]
  37. Price, L.C.; Wenger, L.M. The influence of pressure on petroleum generation and maturation as suggested by aqueous py-rolysis. Org. Geochem. 1992, 19, 141–159. [Google Scholar] [CrossRef]
  38. Cao, Z.; Jiang, H.; Zeng, J.H.; Saibi, H.; Lu, T.Z.; Xie, X.M.; Zhang, Y.C.; Zhou, G.G.; Wu, K.Y.; Guo, J.R. Nanoscale liquid hydrocarbon adsorption on clay minerals: A molecular dynamics simulation of shale oils. Chem. Eng. J. 2021, 420, 127578. [Google Scholar] [CrossRef]
  39. Liu, B.; Shi, J.X.; Fu, X.F.; Lyu, Y.F.; Sun, X.D.; Gong, L.; Bai, Y.F. Petrological characteristics and shale oil enrichment of la-custrine fine-grained sedimentary system: A case study of organic-rich shale in first member of Cretaceous Qingshankou Formation in Gulong Sag, Songliao Basin, NE China. Pet. Explor. Dev. 2018, 45, 884–894. [Google Scholar] [CrossRef]
  40. Xu, L.; Chang, Q.S.; Feng, L.L.; Zhang, N.; Liu, H. The reservoir characteristics and control factors of shale oil in Permian 183 Fengcheng Formation of Mahu Sag, Junggar Basin. China Pet. Explor. 2019, 24, 649. [Google Scholar]
  41. Ji, Y.Q.; Qiu, L.M.; Xia, J.L.; Zhang, T.W. Micro-pore characteristics and methane adsorption properties of common clay minerals by electron microscope scanning. Acta Petrol. Sin. 2012, 33, 249. [Google Scholar]
  42. Wu, C.J.; Tuo, J.C.; Zhang, L.F.; Zhang, M.F.; Li, J.; Liu, Y.; Qian, Y. Pore characteristics differences between clay-rich and clay-poor shales of the Lower Cambrian Niutitang Formation in the Northern Guizhou area, and insights into shale gas storage mechanisms. Int. J. Coal Geol. 2017, 178, 13–25. [Google Scholar] [CrossRef]
  43. Gao, B.; He, W.Y.; Feng, Z.H.; Shao, H.M.; Zhang, A.D.; Pan, H.F.; Chen, G.L. Lithology, physical property, oil-bearing property and their controlling factors of Gulong shale in Songliao Basin (in Chinese with English abstract). Pet. Geol. Oilfield Dev. Daqing 2022, 41, 68–79. [Google Scholar]
  44. Cao, Y.; Han, H.; Guo, C.; Pang, P.; Ding, Z.-G.; Gao, Y. Influence of extractable organic matters on pore structure and its evolution of Chang 7 member shales in the Ordos Basin, China: Implications from extractions using various solvents. J. Nat. Gas Sci. Eng. 2020, 79, 103370. [Google Scholar] [CrossRef]
  45. Hou, L.-H.; Wu, S.-T.; Jing, Z.-H.; Jiang, X.-H.; Yu, Z.-C.; Hua, G.L.; Su, L.; Yu, C.; Liao, F.-R.; Tian, H. Effects of types and content of clay minerals on reservoir effectiveness for lacustrine organic matter rich shale. Fuel 2022, 327, 125043. [Google Scholar] [CrossRef]
  46. Kwon, O.; Herbert, B.E.; Kronenberg, A.K. Permeability of illite-bearing shale: 2. Influence of fluid chemistry on flow and functionally connected pores. J. Geophys. Res. Earth Surf. 2004, 109. [Google Scholar] [CrossRef]
  47. Meng, Z.; Sun, W.; Liu, Y.; Luo, B.; Zhao, M. Effect of pore networks on the properties of movable fluids in tight sandstones from the perspective of multi-techniques. J. Pet. Sci. Eng. 2021, 201, 108449. [Google Scholar] [CrossRef]
  48. Kim, Y.; Lee, C.; Lee, E.Y. Numerical analysis of sedimentary compaction: Implications for porosity and layer thickness variation. J. Geol. Soc. Korea 2018, 54, 631–640. [Google Scholar] [CrossRef]
  49. Thyberg, B.; Jahren, J. Quartz cementation in mudstones: Sheet-like quartz cement from clay mineral reactions during burial. Pet. Geosci. 2011, 17, 53–63. [Google Scholar] [CrossRef]
  50. Lai, D.G.; Chen, Z.H.; Lin, L.X.; Zhang, Y.M.; Gao, S.Q.; Xu, G.W. Secondary cracking and upgrading of shale oil from py-ro-lyzing oil shale over shale ash. Energy Fuels 2015, 29, 2219–2226. [Google Scholar] [CrossRef]
  51. Ariskina, K.A.; Yuan, C.D.; Abaas, M.; Emelianov, D.A.; Rodionov, N.; Varfolomeev, M.A. Catalytic effect of clay rocks as natural catalysts on the combustion of heavy oil. Appl. Clay Sci. 2020, 193, 105662. [Google Scholar] [CrossRef]
  52. Zhou, C.H. An overview on strategies towards clay-based designer catalysts for green and sustainable catalysis. Appl. Clay Sci. 2011, 53, 87–96. [Google Scholar] [CrossRef]
  53. Rahman, H.M.; Kennedy, M.; Löhr, S.; Dewhurst, D.N.; Sherwood, N.; Yang, S.Y.; Horsfield, B. The influence of shale depo-sitional fabric on the kinetics of hydrocarbon generation through control of mineral surface contact area on clay catalysis. Geochim. Cosmochim. Acta 2018, 220, 429–448. [Google Scholar] [CrossRef]
  54. Zhu, X.J.; Cai, J.G.; Wang, G.L.; Song, M.S. Role of organo-clay composites in hydrocarbon generation of shale. Int. J. Coal Geol. 2018, 192, 83–90. [Google Scholar] [CrossRef]
  55. Wei, H.B.; Zhang, Y.P.; Cui, J.H.; Han, L.L.; Li, Z.Q. Engineering and environmental evaluation of silty clay modified by waste fly ash and oil shale ash as a road subgrade material. Constr. Build. Mater. 2019, 196, 204–213. [Google Scholar] [CrossRef]
  56. Williams, P.F. Thermogravimetry and decomposition kinetics of British Kimmeridge Clay oil shale. Fuel 1985, 64, 540–545. [Google Scholar] [CrossRef]
  57. Jurg, J.W.; Eisma, E. Petroleum Hydrocarbons: Generation from Fatty Acid. Science 1964, 144, 1451–1452. [Google Scholar] [CrossRef] [PubMed]
  58. Song, R.R.; Meng, X.L.; Yu, C.; Bian, J.J.; Su, J.Z. Oil shale in-situ upgrading with natural clay-based catalysts: Enhancement of oil yield and quality. Fuel 2022, 314, 123076. [Google Scholar] [CrossRef]
  59. Wu, T.; Pan, Z.; Liu, B.; Connell, L.D.; Sander, R.; Fu, X. Laboratory characterization of shale oil storage behavior: A com-prehensive review. Energy Fuels 2021, 35, 7305–7318. [Google Scholar] [CrossRef]
  60. Su, S.; Jiang, Z.; Xuanlong, S.; Zhang, C.; Zou, Q.; Li, Z.; Zhu, R. The effects of shale pore structure and mineral components on shale oil accumulation in the Zhanhua Sag, Jiyang Depression, Bohai Bay Basin, China. J. Pet. Sci. Eng. 2018, 165, 365–374. [Google Scholar] [CrossRef]
Energies 15 06712 g001 550
Figure 1. Location of the Songliao Basin on a generalized map (A). Central Depression (B). Well locations in the study area (the Gulong Sag) (C). (Modified from Liu et al., 2019 [27]).
Figure 1. Location of the Songliao Basin on a generalized map (A). Central Depression (B). Well locations in the study area (the Gulong Sag) (C). (Modified from Liu et al., 2019 [27]).
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Figure 2. The strata and sedimentary characteristics of the Qingshankou Formation in western Songliao Basin (modified from Liu et al., 2019 [27]).
Figure 2. The strata and sedimentary characteristics of the Qingshankou Formation in western Songliao Basin (modified from Liu et al., 2019 [27]).
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Figure 3. 3D reconstruction of the S41 shale sample.
Figure 3. 3D reconstruction of the S41 shale sample.
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Figure 4. 3D reconstruction of S41 sample after threshold segmentation.
Figure 4. 3D reconstruction of S41 sample after threshold segmentation.
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Figure 5. The schematic diagram of the experimental procedure of the studied sample.
Figure 5. The schematic diagram of the experimental procedure of the studied sample.
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Figure 6. Relationship between porosity and permeability in studied samples. (a) Relationship between total porosity and continuous porosity of the studied samples. (b) The crossplot of total porosity versus permeability for the studied samples. (c) The relationship between continuous porosity and permeability of the studied samples. (d) The relationship between total porosity and burial depth of the studied samples.
Figure 6. Relationship between porosity and permeability in studied samples. (a) Relationship between total porosity and continuous porosity of the studied samples. (b) The crossplot of total porosity versus permeability for the studied samples. (c) The relationship between continuous porosity and permeability of the studied samples. (d) The relationship between total porosity and burial depth of the studied samples.
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Figure 7. (a) Crossplot of total porosity versus clay content for the studied samples. (b) Continuous porosity versus clay content of the studied samples.
Figure 7. (a) Crossplot of total porosity versus clay content for the studied samples. (b) Continuous porosity versus clay content of the studied samples.
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Figure 8. (a) The relationship between total porosity and oil content, S1 peak of the studied samples. (b) The relationship between continuous porosity and the oil-bearing capacity, S1, of the studied samples. (c) The relationship between permeability and the oil content (S1) of the studied samples. (d) The relationship between clay mineral content and the oil content (S1) of the studied samples.
Figure 8. (a) The relationship between total porosity and oil content, S1 peak of the studied samples. (b) The relationship between continuous porosity and the oil-bearing capacity, S1, of the studied samples. (c) The relationship between permeability and the oil content (S1) of the studied samples. (d) The relationship between clay mineral content and the oil content (S1) of the studied samples.
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Table
Table 1. Mineralogy and oil-bearing characteristics of the studied samples.
Table 1. Mineralogy and oil-bearing characteristics of the studied samples.
SampleDepthMemberLithological DescriptionQuartz (wt.%)Potassium Feldspar (wt.%)Plagioclase (wt.%)Calcite (wt.%)Iron Dolomite (wt.%)Siderite (wt.%)Pyrite (wt.%)Clay Mineral (wt.%)S1 (mg/g)
S412335.8K1qn1Dark gray lamellar shale340.919.51.33.90.63.835.96.73
S1892409.8K1qn1Dark gray lamellar shale32.20.618.11.72.11.35.438.68.54
S2012415.1K1qn1Dark gray lamellar shale29.91.223.45.20.01.36.032.91.36
S3172473.93K1qn1Dark gray lamellar shale32.92.621.68.60.00.01.932.63.48
S3532491.8K1qn1Dark gray lamellar shale33.02.018.20.30.00.04.242.38.26
Table
Table 2. Total porosity, continuous porosity and permeability of the studied samples.
Table 2. Total porosity, continuous porosity and permeability of the studied samples.
SampleS41S189S201S317S353Average
Continuous porosity %4.837.790.844.146.204.76
Total porosity %10.1612.031.876.3111.058.28
Continuous pore/Total pore %47.5464.7544.9265.6156.1155.79
Permeability (10−3 μm2)1.6662.2840.0611.5241.4961.137
Table
Table 3. The porosity and permeability of the sample with typical equipment.
Table 3. The porosity and permeability of the sample with typical equipment.
SampleS41S189S201S317S353Average
Porosity %9.3911.261.225.1110.487.492
Permeability (10−3 μm2)1.5632.2110.0541.2311.3211.276
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Cao, Y.; Wu, Q.; Jin, Z.; Zhu, R. Characterization of Microstructures in Lacustrine Organic-Rich Shale Using Micro-CT Images: Qingshankou Formation in Songliao Basin. Energies 2022, 15, 6712. https://doi.org/10.3390/en15186712

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Cao Y, Wu Q, Jin Z, Zhu R. Characterization of Microstructures in Lacustrine Organic-Rich Shale Using Micro-CT Images: Qingshankou Formation in Songliao Basin. Energies. 2022; 15(18):6712. https://doi.org/10.3390/en15186712

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Cao, Yan, Qi Wu, Zhijun Jin, and Rukai Zhu. 2022. "Characterization of Microstructures in Lacustrine Organic-Rich Shale Using Micro-CT Images: Qingshankou Formation in Songliao Basin" Energies 15, no. 18: 6712. https://doi.org/10.3390/en15186712

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